How to Implement a Tree of Thoughts in Python

pip install anthropic python-dotenv

ANTHROPIC_API_KEY=YOUR_ANTHROPIC_API_KEY

import os
from dotenv import load_dotenv
from anthropic import Anthropic # Load environment variables from .env file
load_dotenv() MAX_TOKENS = 1024
DEFAULT_TEMPERATURE = 0
DEFAULT_ANTHROPIC_MODEL_NAME = "claude-3-5-sonnet-20240620" class AnthropicService: def __init__(self, model_name: str = None, anthropic: Anthropic = None): if not os.environ.get("ANTHROPIC_API_KEY"): raise ValueError("No valid API key found for Anthropic.") self.client = anthropic or Anthropic( api_key=os.environ["ANTHROPIC_API_KEY"]) self.model_name = model_name or DEFAULT_ANTHROPIC_MODEL_NAME def generate_response(self, prompt: str, max_tokens: int = MAX_TOKENS, temperature: float = DEFAULT_TEMPERATURE) -> str: msg = self.client.messages.create( model=self.model_name, max_tokens=max_tokens, temperature=temperature, messages=[ { "role": "user", "content": prompt, }, ], ) return msg.content[0].text

from ai import AnthropicService

class ThoughtNode: def __init__(self, thought, children=None): self.thought = thought self.children = children or []

class TreeOfThought: def __init__(self, root_prompt, ai_service=None, max_iterations=3, max_tokens=250): self.root = ThoughtNode(root_prompt) self.max_iterations = max_iterations self.ai_service = ai_service or AnthropicService() self.current_thoughts = [self.root] self.max_tokens = max_tokens

 def call_llm(self, prompt): try: response = self.ai_service.generate_response( prompt, max_tokens=self.max_tokens, ) return response except Exception as e: print(f"Error calling LLM: {e}") return []

 def explore_thoughts(self, thought_nodes): new_thought_nodes = [] for thought_node in thought_nodes: prompt = f"Given the current thought: '{thought_node.thought}', provide two concise next thoughts that evolve this idea further." response = self.call_llm(prompt) if response: new_thought_node = ThoughtNode(response) thought_node.children.append(new_thought_node) new_thought_nodes.append(new_thought_node) return new_thought_nodes

 def run(self): iteration = 0 while self.current_thoughts and iteration < self.max_iterations: print(f"Iteration {iteration + 1}:") self.current_thoughts = self.explore_thoughts( self.current_thoughts) for thought_node in self.current_thoughts: print(f"Explored Thought: {thought_node.thought}") iteration += 1

 def update_starting_thought(self, new_thought): self.root = ThoughtNode(new_thought) self.current_thoughts = [self.root] def print_tree(self, node, level=0): indent = ' ' * (level * 2) thought_lines = node.thought.split('\n') for idx, line in enumerate(thought_lines): if idx == 0: print(f"{indent}- {line}") else: print(f"{indent} {line}") for child in node.children: self.print_tree(child, level + 1)

if __name__ == "__main__": starting_prompt = "Think of a solution to reduce the operational costs of your business." tot = TreeOfThought(starting_prompt) tot.run() print("=" * 100) print("Final Tree of Thoughts:") tot.print_tree(tot.root)

Final Tree of Thoughts:
- Think of a solution to reduce the operational costs of your business. - Here are two concise next thoughts that evolve the idea of reducing operational costs: 1. Analyze energy consumption and implement efficiency measures. 2. Explore automation options for repetitive tasks to reduce labor costs. - Here are two concise next thoughts that further evolve the idea of reducing operational costs: 1. Implement a lean inventory management system to minimize holding costs and waste. 2. Negotiate better terms with suppliers and consider strategic partnerships for bulk purchasing discounts. - Here are two concise next thoughts that further evolve the idea of reducing operational costs: 1. Adopt energy-efficient technologies and practices to lower utility expenses and reduce environmental impact. 2. Invest in automation and AI-driven processes to streamline operations and reduce labor costs in the long term.